From this behavior, it may be expected that all stationary points of functions which satisfy the RSI are global minima, which is indeed true [1]}. However, note that we impose the RSI condition on \(\mathrm {Err}_{n} (*)\)  (which cannot be computed by the learner) and not the empirical risk \(\mathcal {L}_n (\) . Even under the RSI condition on \(\mathrm {Err}_{n} (*)\) , it is not trivial to find global minima of the training objective, \(\mathcal {L}_n ( + R(\)  which is composed of two non-convex (in \() functions and is non-smooth. Thus, we restrict our attention to the analysis of \textit {first-order stationary points} of this objective.\)
